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Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet
Image and Video Processing · Submitted 25 Sep 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
In treating gastrointestinal cancer using radiotherapy, the role of the radiation oncologist is to administer high doses of radiation, through x-ray beams, toward the tumor while avoiding the stomach and intestines. With the advent of precise radiation treatment technology such as the MR-Linac, oncologists can visualize the daily positions of the tumors and intestines, which may vary day to day. Before delivering radiation, radio oncologists must manually outline the position of the gastrointestinal organs in order to determine position and direction of the x-ray beam. This is a time consuming and labor intensive process that may substantially prolong a patient's treatment. A deep learning (DL) method can automate and expedite the process. However, many deep neural networks approaches currently in use are black-boxes which lack interpretability which render them untrustworthy and impractical in a healthcare setting. To address this, an emergent field of AI known as Explainable AI (XAI) may be incorporated to improve the transparency and viability of a model. This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In treating gastrointestinal cancer using radiotherapy, the role of the radiation oncologist is to administer high doses of radiation, through x-ray beams, toward the tumor while avoiding the stomach and intestines. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | With the advent of precise radiation treatment technology such as the MR-Linac, oncologists can visualize the daily positions of the tumors and intestines, which may vary day to day. | background 1.00 | 0.05 | 0.02 | hedged possibility 0.56 |
| s2 | Before delivering radiation, radio oncologists must manually outline the position of the gastrointestinal organs in order to determine position and direction of the x-ray beam. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s3 | This is a time consuming and labor intensive process that may substantially prolong a patient's treatment. | background 0.93 | 0.07 | 0.02 | hedged possibility 0.82 |
| s4 | A deep learning (DL) method can automate and expedite the process. | background 0.81 | 0.06 | 0.06 | asserted 0.77 |
| s5 | However, many deep neural networks approaches currently in use are black-boxes which lack interpretability which render them untrustworthy and impractical in a healthcare setting. | gap 0.68 | 0.06 | 0.03 | asserted 0.99 |
| s6 | To address this, an emergent field of AI known as Explainable AI (XAI) may be incorporated to improve the transparency and viability of a model. | background 0.76 | 0.05 | 0.06 | hedged possibility 1.00 |
| s7 | This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation. | contribution 1.00 | 0.80 | 0.34 | asserted 0.99 |
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The paper
Original abstract
In treating gastrointestinal cancer using radiotherapy, the role of the radiation oncologist is to administer high doses of radiation, through x-ray beams, toward the tumor while avoiding the stomach and intestines. With the advent of precise radiation treatment technology such as the MR-Linac, oncologists can visualize the daily positions of the tumors and intestines, which may vary day to day. Before delivering radiation, radio oncologists must manually outline the position of the gastrointestinal organs in order to determine position and direction of the x-ray beam. This is a time consuming and labor intensive process that may substantially prolong a patient's treatment. A deep learning (DL) method can automate and expedite the process. However, many deep neural networks approaches currently in use are black-boxes which lack interpretability which render them untrustworthy and impractical in a healthcare setting. To address this, an emergent field of AI known as Explainable AI (XAI) may be incorporated to improve the transparency and viability of a model. This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation.
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